Improving adaptive network fuzzy inference system with Levenberg-Marquardt algorithm

机译：用Levenberg-Marquardt算法改进自适应网络模糊推理系统

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The well-known adaptive neuro-fuzzy inference system (ANFIS) uses a combination of least-square estimation (LSE) and gradient descent back-propagation methods to model a training data set. In this paper, we show that the rate of convergence of ANFIS can be very much improved by using a combination of LSE and Levenberg-Marquardt algorithm (LMA). The improved ANFIS converges more closely and significantly more rapidly to the data. Detail explanation of the proposed ANFIS is presented, and its validity is verified via simulation.

机译：众所周知的自适应神经模糊推理系统（ANFIS）使用最小二乘估计（LSE）和梯度下降反向传播方法的组合来对训练数据集进行建模。在本文中，我们表明通过结合使用LSE和Levenberg-Marquardt算法（LMA），可以大大提高ANFIS的收敛速度。改进后的ANFIS可以更紧密，更快地收敛到数据。提出了对所提出的ANFIS的详细解释，并通过仿真验证了其有效性。

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《Annual IEEE International Systems Conference》|2017年|1-6|共6页
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Ana Farhat; Ka C Cheok;
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backpropagation; fuzzy neural nets; fuzzy reasoning; gradient methods; least squares approximations;

机译：反向传播;模糊神经网络;模糊推理;梯度法;最小二乘近似;

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Improving adaptive network fuzzy inference system with Levenberg-Marquardt algorithm

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